The invention relates to the field of medical imaging.
Medical imaging may be used to capture digital images of tumors, and the tumor objects in the digital image identified, such as by defining and/or calculating a binary image mask with one value for the tumor object, such as 1, 255, −1, or the like, and a second value for the background, such as 0, −1, 255, or the like. The identified tumor image (digital image and mask) may be further processed to compute features of the tumor, such as mean intensity, relative intensity, entropy, texture, and/or the like, that in turn can be used to classify the tumor. The tumor classification is important for diagnosis, prognosis, treatment, and/or the like.
Machine learning techniques may be applied to identified tumors, where a training set is used to develop extraction methods for feature extraction. The extraction methods are used to extract features from a patient's tumor image. For example, computer vision texture analysis tools are used to extract features from a tumor image.
For example, oriented gradients are used to extract features from tumor images, as described by Dalal et al. in “Histograms of Oriented Gradients for Human Detection” published in the proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, San Diego, Calif., USA (DOI: 10.1109/CVPR.2005.177). For example, wavelet-based textures are used to extract features from tumor images, as described by Do et al. in “Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance” published in IEEE transactions on image processing, 2002, volume: 11, Issue: 2, page(s): 146-158 (DOI: 10.1109/83.982822). For example, grey level co-occurrence matrices are used to extract features from tumor images, as described by Haralick et al. in “Textural Features for Image Classification”, published in IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-3, 1973, pp. 610-621 (DOI: 10.1109/TSMC.1973.4309314). For example, deep neural networks are used to extract features from tumor images, as described by Krizhevsky et al. in “Imagenet classification with deep convolutional neural networks” published in the proceedings of Proceedings of the 25th International Conference on Neural Information Processing Systems—Volume 1, 2012, Pages 1097-1105.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.